Source, dispersion and combustion modelling of an accidental release of hydrogen in an urban environment

Hydrogen is likely to be the most important future energy carrier, for many stationary and mobile applications, with the potential to make significant reductions in greenhouse gas emissions especially if renewable primary energy sources are used to produce the hydrogen. A safe transition to the use of hydrogen by members of the general public requires that the safety issues associated with hydrogen applications have to be investigated and fully understood. In order to assess the risks associated with hydrogen applications, its behaviour in realistic accident scenarios has to be predicted, allowing mitigating measures to be developed where necessary. A key factor in this process is predicting the release, dispersion and combustion of hydrogen in appropriate scenarios. This paper illustrates an application of CFD methods to the simulation of an actual hydrogen explosion. The explosion occurred on 3 March 1983 in a built up area of central Stockholm, Sweden, after the accidental release of approximately 13.5 kg of hydrogen from a rack of 18 interconnected 50 l industrial pressure vessels (200 bar working pressure) being transported by a delivery truck. Modelling of the source term, dispersion and combustion were undertaken separately using three different numerical tools, due to the differences in physics and scales between the different phenomena. Results from the dispersion calculations together with the official accident report were used to identify a possible ignition source and estimate the time at which ignition could have occurred. Ignition was estimated to occur 10s after the start of the release, coinciding with the time at which the maximum flammable hydrogen mass and cloud volume were found to occur (4.5 kg and 600 m(3), respectively). The subsequent simulation of the combustion adopts initial conditions for mean flow and turbulence from the dispersion simulations, and calculates the development of a fireball. This provides physical values, e.g. maximum overpressure and far-field overpressure that may be used as a comparison with the known accident details to give an indication of the validity of the models. The simulation results are consistent with both the reported near-field damage to buildings and persons and with the far-field damage to windows. The work was undertaken as part of the European Integrated Hydrogen Project-Phase 2 (EIHP2) with partial funding from the European Commission via the Fifth Framework Programme.     

Stability of feature selection algorithms: a study on high-dimensional spaces

With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally, we show how stability profiles can support the choice of a feature selection algorithm. Alexandros Kalousis received the B.Sc. degree in computer science, in 1994, and the M.Sc. degree in advanced information systems, in 1997, both from the University of Athens, Greece. He received the Ph.D. degree in meta-learning for classification algorithm selection from the University of Geneva, Department of Computer Science, Geneva, in 2002. Since then he is a Senior Researcher in the same university. His research interests include relational learning with kernels and distances, stability of feature selection algorithms, and feature extraction from spectral data. Julien Prados is a Ph.D. student at the University of Geneva, Switzerland. In 1999 and 2001, he received the B.Sc. and M.Sc. degrees in computer science from the University Joseph Fourier (Grenoble, France). After a year of work in industry, he joined the Geneva Artificial Intelligence Laboratory, where he is working on bioinformatics and datamining tools for mass spectrometry data analysis. Melanie Hilario has a Ph.D. in computer science from the University of Paris VI and currently works at the University of Geneva’s Artificial Intelligence Laboratory. She has initiated and participated in several European research projects on neuro-symbolic integration, meta-learning, and biological text mining. She has served on the program committees of many conferences and workshops in machine learning, data mining, and artificial intelligence. She is currently an Associate Editor of theInternational Journal on Artificial Intelligence Toolsand a member of the Editorial Board of theIntelligent Data Analysis journal.     

Approximate analysis of serial flow lines with multiple parallel-machine stations

This paper studies serial flow lines, in which each station consists of multiple identical reliable parallel machines. The parallel machines of different work stations are not necessarily identical, viz., station processing times are assumed to be exponentially distributed with non-identical mean service rates. Initially, a model consisting of two stations with multiple parallel machines and an intermediate buffer is solved analytically, by developing a recursive algorithm that generates the transition matrix for any value of the intermediate buffer capacity. This model is used as a decomposition block for solving larger lines. More specifically, the decomposition block is solved via exact Markovian analysis and then the decomposition equations and an algorithm that simultaneously solves them are derived in order to evaluate the performance measures of large production systems with multiple parallel-machine stations. Numerical results are provided for large production lines with up to 1000 workstations. These results are compared against simulation and the average percentage error is found to be very small.     

Dynamic action recognition based on dynemes and Extreme Learning Machine

In this paper, we propose a novel method that performs dynamic action classification by exploiting the effectiveness of the Extreme Learning Machine (ELM) algorithm for single hidden layer feedforward neural networks training. It involves data grouping and ELM based data projection in multiple levels. Given a test action instance, a neural network is trained by using labeled action instances forming the groups that reside to the test sample’s neighborhood. The action instances involved in this procedure are, subsequently, mapped to a new feature space, determined by the trained network outputs. This procedure is performed multiple times, which are determined by the test action instance at hand, until only a single class is retained. Experimental results denote the effectiveness of the dynamic classification approach, compared to the static one, as well as the effectiveness of the ELM in the proposed dynamic classification setting.     

Silhouette-based human action recognition using sequences of key poses

In this paper, a human action recognition method is presented in which pose representation is based on the contour points of the human silhouette and actions are learned by making use of sequences of multi-view key poses. Our contribution is twofold. Firstly, our approach achieves state-of-the-art success rates without compromising the speed of the recognition process and therefore showing suitability for online recognition and real-time scenarios. Secondly, dissimilarities among different actors performing the same action are handled by taking into account variations in shape (shifting the test data to the known domain of key poses) and speed (considering inconsistent time scales in the classification). Experimental results on the publicly available Weizmann, MuHAVi and IXMAS datasets return high and stable success rates, achieving, to the best of our knowledge, the best rate so far on the MuHAVi Novel Actor test.     

Automatic speech recognition performance in different room acoustic environments with and without dereverberation preprocessing

The performance of recent dereverberation methods for reverberant speech preprocessing prior to Automatic Speech Recognition (ASR) is compared for an extensive range of room and source-receiver configurations. It is shown that room acoustic parameters such as the clarity (C50) and the definition (D50) correlate well with the ASR results. When available, such room acoustic parameters can provide insight into reverberant speech ASR performance and potential improvement via dereverberation preprocessing. It is also shown that the application of a recent dereverberation method based on perceptual modelling can be used in the above context and achieve significant Phone Recognition (PR) improvement, especially under highly reverberant conditions.     

Evaluation of optical core networks based on the CANON architecture

Clustering of nodes in optical networks has been proven to be an efficient way to serve end-to-end connectivity. However, clustering requires specific topological characteristics, or alternatively the introduction of significant alterations of an existing topology to achieve the expected performance improvements. The comparison of future dynamic optical networking technologies should therefore include in the set of initial assumptions, apart from the statistical properties of the traffic load, the network topology to draw conclusions regarding the efficiency as well as feasibility and scalability of the proposed solutions. In this article, we show how node clustering under the CANON architecture can be applied in real-life core networks and provide superior performance compared to conventional burst switching techniques in terms of blocking, resource utilization and power consumption.     

An Efficient Location Management Protocol for Wireless ATM Customer Premises Networks

In this paper we present a new location management protocol for wireless ATM networks, called LMCP (Location Management and Control Protocol). This protocol is based on the PNNI (Private Network-to-Network Interface) routing functionality to advertise the movement of mobile terminals within predefined areas. Moreover, LMCP uses specialized entities to store and retrieve the current location area of the mobile terminals. These entities are located in mobility enhanced switches that control the execution of mobility procedures (e.g., handovers).The main benefit from the application of LMCP is the establishment of connections that do not contain any misrouted segments. Furthermore, it requires minor modifications to the PNNI and enables its inter-working with other location management mechanisms. The protocol is compared with other similar mechanisms, and its efficiency is demonstrated by the results of an analytical model.     

Development of an automated testing system for vehicle infotainment system

A current premium vehicle is implemented with a variety of information, entertainment, and communication functions, which are generally referred as an infotainment system. During vehicle development, testing of the infotainment system at an overall level is conventionally carried out manually by an expert who can observe at a customer level. This approach has significant limitations with regard to test coverage and effectiveness due to the complexity of the system functions and human’s capability. Hence, it is highly demanded by car manufacturers for an automated infotainment testing system, which replicates a human expert encompassing relevant sensory modalities relating to control (i.e., touch) and observation (i.e., sight and sound) of the system under test. This paper describes the design, development, and evaluation of such a system that consists of simulation of vehicle network, vision-based inspection, automated navigation of features, random cranking waveform generation, sound detection, and test automation. The system developed is able to: stimulate a vehicle system across a wide variety of initialisation conditions, exercise each function, check for system responses, and record failure situations for post-testing analysis.     

Assessing the sustainability of the UK society using thermodynamic concepts: Part 1

This paper provides a concise overview of the influence of human activity within the UK society on resource consumption and the subsequent effects on the environment. The concept of the Multi Scale Integrated Analysis of Societal Metabolism (MSIASM) is applied in order to elucidate the evolution of the UK economy for the period between 1981 and 2004. Our findings highlight the transition to a service-based economy and the disproportionate increase of energy demand when compared to the overall population increase. Emergy synthesis is applied in order to understand the production and consumption patterns and the environmental support required to sustain human activity within the UK for the year 2004. Generally speaking the UK society greatly benefits from its significant natural resources with 44.3% of the total emergy used coming from home sources and 29.1% from locally renewable sources. Interestingly enough, despite its significant natural resources, the UK economy, seems to be a net emergy importer by 638.5 × 10²¹ seJ. Furthermore, the current economic activity is believed to have a significant impact in the environment despite the relatively low environmental load ratio of 2.44.